Task and Motion Planning (TAMP) has made strides in complex manipulation tasks, yet the execution robustness of the planned solutions remains overlooked. In this work, we propose a method for reactive TAMP to cope with runtime uncertainties and disturbances. We combine an Active Inference planner (AIP) for adaptive high-level action selection and a novel Multi-Modal Model Predictive Path Integral controller (M3P2I) for low-level control. This results in a scheme that simultaneously adapts both high-level actions and low-level motions. The AIP generates alternative symbolic plans, each linked to a cost function for M3P2I. The latter employs a physics simulator for diverse trajectory rollouts, deriving optimal control by weighing the different samples according to their cost. This idea enables blending different robot skills for fluid and reactive plan execution, accommodating plan adjustments at both the high and low levels to cope, for instance, with dynamic obstacles or disturbances that invalidate the current plan. We have tested our approach in simulations and real-world scenarios.

翻译：任务与运动规划（TAMP）在复杂操作任务中已取得显著进展，然而规划方案在执行层面的鲁棒性仍被忽视。本研究提出一种响应式TAMP方法以应对运行时的不确定性与干扰。我们将用于自适应高层动作选择的主动推理规划器（AIP）与用于底层控制的新型多模态模型预测路径积分控制器（M3P2I）相结合，构建出能同时调整高层动作与底层运动的框架。AIP生成多个备选符号化规划，每个规划对应M3P2I的一个代价函数。后者利用物理模拟器进行多样化轨迹推演，通过依据代价对样本加权计算得出最优控制。该机制支持融合不同机器人技能以实现流畅且响应式的规划执行，能够在高层与底层同时进行规划调整，以应对动态障碍物或使当前规划失效的干扰等场景。我们已在仿真环境与真实场景中验证了所提方法。